🍨 本文为🔗365天深度学习训练营 中的学习记录博客
🍖 原作者：K同学啊|接辅导、项目定制
1 理论基础
Inception v3论文
Inception v3由谷歌研究员Christian Szegedy等人在2015年的论文《Rethinking the Inception Architecture for Computer Vision》中提出。Inception v3是Inception网络系列的第三个版本，它在ImageNet图像识别竞赛中取得了优异成绩，尤其是在大规模图像识别任务中表现出色。
Inception v3的主要特点如下：
1、更深的网络结构：Inception v3比之前的Inception网络结构更深，包含了48层卷积层。这使得网络可以提取更多层次的特征，从而在图像识别任务上取得更好的效果。
2、使用Factorized Convolutions：Inception v3采用了Factorized Convolutions（分解卷积），将较大的卷积核分解为多个较小卷积核。这种方法可以降低网络的参数数量，减少计算复杂度，同时保持良好的性能。
3、使用Batch Normalization：Inception v3在每个卷积层之后都添加了Batch Normalization（BN），这有助于网络的收敛和泛化能力。BN可以减少Internal Covariate Shift（内部协变量偏移）现象，加快训练速度，同时提高模型的鲁棒性。
4、辅助分类器：Inception v3引入了辅助分类器，可以在网络训练过程中提供额外的梯度信息，帮助网络更好的学习特征。辅助分类器位于网络的某个中间层，其输出会与主分类器的输出进行加权融合，从而得到最终的预测结果。
5、基于RMSProp的优化器：Inception v3使用了RMSProp优化器进行训练。相比于传统的随机梯度下降（SGD）方法，RMSProp可以自适应地调整学习率，使得训练过程更加稳定，收敛速度更快。
Inception v3在图像分类、物体检测和图像分割等计算机视觉任务中均取得了显著的效果。然而，由于其较大的网络结构和计算复杂度，Inception v3在实际应用中可能需要较高的硬件要求。
相对于Inception v1的Inception Module结构，Inception v3中做出了如下改动：
（1）将5x5的卷积分解为两个3x3的卷积运算已提升计算速度。尽管这有点违反直觉，但一个5x5的卷积在计算成本上是一个3x3卷积的2.78倍。所以叠加两个3x3卷积实际上在性能上会有所提升，如下图所示：
（2）此外，作者将nxn的卷积核尺寸分解为1xn和nx1两个卷积，例如， 一个3x3的卷积等价于首先执行一个1x3的卷积，再执行一个3x1的卷积。他们还发现这种方法在成本上要比单个3x3的卷积降低33%，这一结构如下图所示：
此处如果n=3，则与上一张图像一致。最左侧的5x5卷积可被表示为两个3x3卷积，太慢又可以表示为1x3和3x1卷积。
模块中的滤波器组被扩展（即变得更宽而不是更深），以解决表征性瓶颈。如果该模块没有被拓展宽度，而是变得更深，那么维度会过多减少，造成信息损失。如下图所示：
最后实现的inception v3网络是如下所示：
2 代码实现
2.1 开发环境
电脑系统：ubuntu16.04
编译器：Jupter Lab
语言环境：Python 3.7
深度学习环境：Pytorch
2.2 前期准备
2.2.1 设置GPU
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision
from torchvision import transforms, datasets
import os, PIL, pathlib, warnings
warnings.filterwarnings("ignore")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)2.2.2 导入数据
import os,PIL,random,pathlib
data_dir = '../data/4-data/'
data_dir = pathlib.Path(data_dir)
data_dir
data_paths = list(data_dir.glob('*'))
classNames = [str(path).split('\\')[-1] for path in data_paths]
print('classNames:', classNames , '\n')
total_dir = '../data/4-data/'
train_transforms = transforms.Compose([
transforms.Resize([299, 299]), # resize输入图片
transforms.ToTensor(), # 将PIL Image或numpy.ndarray转换成tensor
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]) # 从数据集中随机抽样计算得到
])
total_data = datasets.ImageFolder(total_dir, transform=train_transforms)
print(total_data, '\n')
print(total_data.class_to_idx)输出结果如下：
2.2.3 划分数据集
train_size = int(0.8 * len(total_data))
test_size = len(total_data) - train_size
train_dataset, test_dataset = torch.utils.data.random_split(total_data, [train_size, test_size])
print(train_dataset, test_dataset)
batch_size = 4
train_dl = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1,
pin_memory=False)
test_dl = torch.utils.data.DataLoader(test_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1,
pin_memory=False)
for X, y in test_dl:
print("Shape of X [N, C, H, W]:", X.shape)
print("Shape of y:", y.shape, y.dtype)
break输出结果如下所示：
2.3 Inception v3的实现
2.3.1 Inception-A
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return F.relu(x, inplace=True)
class InceptionA(nn.Module):
def __init__(self, in_channels, pool_features):
super(InceptionA, self).__init__()
# 1x1 conv branch
self.branch1x1 = BasicConv2d(in_channels, 64, kernel_size=1) #1
self.branch5x5_1 = BasicConv2d(in_channels, 48, kernel_size=1)
self.branch5x5_2 = BasicConv2d(48, 64, kernel_size=5, padding=2)
self.branch3x3dbl_1 = BasicConv2d(in_channels, 64, kernel_size=1)
self.branch3x3dbl_2 = BasicConv2d(64, 96, kernel_size=3, padding=1)
self.branch3x3dbl_3 = BasicConv2d(96, 96, kernel_size=3, padding=1)
self.branch_pool = BasicConv2d(in_channels, pool_features, kernel_size=1)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch5x5 = self.branch5x5_1(x)
branch5x5 = self.branch5x5_2(branch5x5)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
return torch.cat(outputs, 1)2.3.2 Inception-B
class InceptionB(nn.Module):
def __init__(self, in_channels, channels_7x7):
super(InceptionB, self).__init__()
# 1x1 conv branch
self.branch1x1 = BasicConv2d(in_channels, 192, kernel_size=1)
c7 = channels_7x7
self.branch7x7_1 = BasicConv2d(in_channels, c7, kernel_size=1)
self.branch7x7_2 = BasicConv2d(c7, c7, kernel_size=(1,7), padding=(0,3))
self.branch7x7_3 = BasicConv2d(c7, 192, kernel_size=(7,1), padding=(3,0))
self.branch7x7dbl_1 = BasicConv2d(in_channels, c7, kernel_size=1)
self.branch7x7dbl_2 = BasicConv2d(c7, c7, kernel_size=(7,1), padding=(3,0))
self.branch7x7dbl_3 = BasicConv2d(c7, c7, kernel_size=(1,7), padding=(0,3))
self.branch7x7dbl_4 = BasicConv2d(c7, c7, kernel_size=(7,1), padding=(3,0))
self.branch7x7dbl_5 = BasicConv2d(c7, 192, kernel_size=(1,7), padding=(0,3))
self.branch_pool = BasicConv2d(in_channels, 192, kernel_size=1)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch7x7 = self.branch7x7_1(x)
branch7x7 = self.branch7x7_2(branch7x7)
branch7x7 = self.branch7x7_3(branch7x7)
branch7x7dbl = self.branch7x7dbl_1(x)
branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
return torch.cat(outputs, 1)2.3.3 Inception-C
class InceptionC(nn.Module):
def __init__(self, in_channels):
super(InceptionC, self).__init__()
self.branch1x1 = BasicConv2d(in_channels, 320, kernel_size=1) #1
self.branch3x3_1 = BasicConv2d(in_channels, 384, kernel_size=1)
self.branch3x3_2a = BasicConv2d(384, 384, kernel_size=(1,3), padding=(0,1))
self.branch3x3_2b = BasicConv2d(384, 384, kernel_size=(3,1), padding=(1,0))
self.branch3x3dbl_1 = BasicConv2d(in_channels, 448, kernel_size=1)
self.branch3x3dbl_2 = BasicConv2d(448, 384, kernel_size=3, padding=1)
self.branch3x3dbl_3a = BasicConv2d(384, 384, kernel_size=(1,3), padding=(0,1))
self.branch3x3dbl_3b = BasicConv2d(384, 384, kernel_size=(3,1), padding=(1,0))
self.branch_pool = BasicConv2d(in_channels, 192, kernel_size=1)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch3x3 = self.branch3x3_1(x)
branch3x3 = [self.branch3x3_2a(branch3x3),
self.branch3x3_2b(branch3x3),
]
branch3x3 = torch.cat(branch3x3, 1)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = [self.branch3x3dbl_3a(branch3x3dbl),
self.branch3x3dbl_3b(branch3x3dbl),
]
branch3x3dbl = torch.cat(branch3x3dbl, 1)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
return torch.cat(outputs, 1)2.3.4 Reduction-A
class ReductionA(nn.Module):
def __init__(self, in_channels):
super(ReductionA, self).__init__()
self.branch3x3 = BasicConv2d(in_channels, 384, kernel_size=3, stride=2)
self.branch3x3dbl_1 = BasicConv2d(in_channels, 64, kernel_size=1)
self.branch3x3dbl_2 = BasicConv2d(64, 96, kernel_size=3, padding=1)
self.branch3x3dbl_3 = BasicConv2d(96, 96, kernel_size=3, stride=2)
def forward(self, x):
branch3x3 = self.branch3x3(x)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)
outputs = [branch3x3, branch3x3dbl, branch_pool]
return torch.cat(outputs, 1)2.3.5 Reduction-B
class ReductionB(nn.Module):
def __init__(self, in_channels):
super(ReductionB, self).__init__()
self.branch3x3_1 = BasicConv2d(in_channels, 192, kernel_size=1)
self.branch3x3_2 = BasicConv2d(192, 320, kernel_size=3, stride=2)
self.branch7x7x3_1 = BasicConv2d(in_channels, 192, kernel_size=1)
self.branch7x7x3_2 = BasicConv2d(192, 192, kernel_size=(1,7), padding=(0,3))
self.branch7x7x3_3 = BasicConv2d(192, 192, kernel_size=(7,1), padding=(3,0))
self.branch7x7x3_4 = BasicConv2d(192, 192, kernel_size=3, stride=2)
def forward(self, x):
branch3x3 = self.branch3x3_1(x)
branch3x3 = self.branch3x3_2(branch3x3)
branch7x7x3 = self.branch7x7x3_1(x)
branch7x7x3 = self.branch7x7x3_2(branch7x7x3)
branch7x7x3 = self.branch7x7x3_3(branch7x7x3)
branch7x7x3 = self.branch7x7x3_4(branch7x7x3)
branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)
outputs = [branch3x3, branch7x7x3, branch_pool]
return torch.cat(outputs, 1)2.3.6 辅助分支
class InceptionAux(nn.Module):
def __init__(self, in_channels, num_classes):
super(InceptionAux, self).__init__()
self.conv0 = BasicConv2d(in_channels, 128, kernel_size=1)
self.conv1 = BasicConv2d(128, 768, kernel_size=5)
self.conv1.stddev = 0.01
self.fc = nn.Linear(768, num_classes)
self.fc.stddev = 0.001
def forward(self, x):
# 17x17x768
x = F.avg_pool2d(x, kernel_size=5, stride=3)
# 5x5x768
x = self.conv0(x)
# 5x5x128
x = self.conv1(x)
# 1x1x128
x = x.view(x.size(0), -1)
# 768
x = self.fc(x)
# num_classes
return x2.3.7 模型搭建
class InceptionV3(nn.Module):
def __init__(self, num_classes=1000, aux_logits=False, transform_input=False):
super(InceptionV3, self).__init__()
self.aux_logits = aux_logits
self.transform_input = transform_input
self.conv2d_1a_3x3 = BasicConv2d(3, 32, kernel_size=3, stride=2)
self.conv2d_2a_3x3 = BasicConv2d(32, 32, kernel_size=3)
self.conv2d_2b_3x3 = BasicConv2d(32, 64, kernel_size=3, padding=1)
self.conv2d_3b_1x1 = BasicConv2d(64, 80, kernel_size=1)
self.conv2d_4a_3x3 = BasicConv2d(80, 192, kernel_size=3)
self.Mixed_5b = InceptionA(192, pool_features=32)
self.Mixed_5c = InceptionA(256, pool_features=64)
self.Mixed_5d = InceptionA(288, pool_features=64)
self.Mixed_6a = ReductionA(288)
self.Mixed_6b = InceptionB(768, channels_7x7=128)
self.Mixed_6c = InceptionB(768, channels_7x7=160)
self.Mixed_6d = InceptionB(768, channels_7x7=160)
self.Mixed_6e = InceptionB(768, channels_7x7=192)
if aux_logits:
self.AuxLogits = InceptionAux(768, num_classes)
self.Mixed_7a = ReductionB(768)
self.Mixed_7b = InceptionC(1280)
self.Mixed_7c = InceptionC(2048)
self.fc = nn.Linear(2048, num_classes)
def forward(self, x):
if self.transform_input:
x = x.clone()
x[:, 0] = x[:, 0] * (0.229 / 0.5) + (0.485 - 0.5) / 0.5
x[:, 1] = x[:, 1] * (0.224 / 0.5) + (0.456 - 0.5) / 0.5
x[:, 2] = x[:, 2] * (0.225 / 0.5) + (0.406 - 0.5) / 0.5
# 299*299*3
x = self.conv2d_1a_3x3(x)
# 149*149*32
x = self.conv2d_2a_3x3(x)
# 147*147*32
x = self.conv2d_2b_3x3(x)
# 147*147*64
x = F.max_pool2d(x, kernel_size=3, stride=2)
# 73*73*64
x = self.conv2d_3b_1x1(x)
# 73*73*80
x = self.conv2d_4a_3x3(x)
# 71*71*192
x = F.max_pool2d(x, kernel_size=3, stride=2)
# 35*35*192
x = self.Mixed_5b(x)
# 35*35*256
x = self.Mixed_5c(x)
# 35*35*288
x = self.Mixed_5d(x)
# 35*35*288
x = self.Mixed_6a(x)
# 17*17*768
x = self.Mixed_6b(x)
# 17*17*768
x = self.Mixed_6c(x)
# 17*17*768
x = self.Mixed_6d(x)
# 17*17*768
x = self.Mixed_6e(x)
# 17*17*768
if self.training and self.aux_logits:
aux = self.AuxLogits(x)
# 17*17*768
x = self.Mixed_7a(x)
# 8*8*1280
x = self.Mixed_7b(x)
# 8*8*2048
x = self.Mixed_7c(x)
# 8*8*2048
x = F.avg_pool2d(x, kernel_size=8)
# 1*1*2048
x = F.dropout(x, training=self.training)
# 1*1*2048
x = x.view(x.size(0), -1)
# 2048
x = self.fc(x)
# num_classes
if self.training and self.aux_logits:
return x, aux
return x2.3.8 查看模型详情
# 统计模型参数量以及其他指标
import torchsummary
# 调用并将模型转移到GPU中
model = InceptionV3().to(device)
# 显示网络结构
torchsummary.summary(model, (3, 299, 299))
print(model)输出结果如下（由于内容较多，只展示前后部分内容）：
（中间内容省略）
（中间内容省略）
2.4 训练模型
2.4.1 编写训练函数
# 训练循环
def train(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset) # 训练集的大小
num_batches = len(dataloader) # 批次数目, (size/batch_size，向上取整)
train_loss, train_acc = 0, 0 # 初始化训练损失和正确率
for X, y in dataloader: # 获取图片及其标签
X, y = X.to(device), y.to(device)
# 计算预测误差
pred = model(X) # 网络输出
loss = loss_fn(pred, y) # 计算网络输出pred和真实值y之间的差距，y为真实值，计算二者差值即为损失
# 反向传播
optimizer.zero_grad() # grad属性归零
loss.backward() # 反向传播
optimizer.step() # 每一步自动更新
# 记录acc与loss
train_acc += (pred.argmax(1) == y).type(torch.float).sum().item()
train_loss += loss.item()
train_acc /= size
train_loss /= num_batches
return train_acc, train_loss2.4.2 编写测试函数
def test(dataloader, model, loss_fn):
size = len(dataloader.dataset) # 训练集的大小
num_batches = len(dataloader) # 批次数目, (size/batch_size，向上取整)
test_loss, test_acc = 0, 0 # 初始化测试损失和正确率
# 当不进行训练时，停止梯度更新，节省计算内存消耗
# with torch.no_grad():
for imgs, target in dataloader: # 获取图片及其标签
with torch.no_grad():
imgs, target = imgs.to(device), target.to(device)
# 计算误差
tartget_pred = model(imgs) # 网络输出
loss = loss_fn(tartget_pred, target) # 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失
# 记录acc与loss
test_loss += loss.item()
test_acc += (tartget_pred.argmax(1) == target).type(torch.float).sum().item()
test_acc /= size
test_loss /= num_batches
return test_acc, test_loss2.4.3 正式训练
import copy
optimizer = torch.optim.Adam(model.parameters(), lr = 1e-4)
loss_fn = nn.CrossEntropyLoss() #创建损失函数
epochs = 40
train_loss = []
train_acc = []
test_loss = []
test_acc = []
best_acc = 0 #设置一个最佳准确率，作为最佳模型的判别指标
if hasattr(torch.cuda, 'empty_cache'):
torch.cuda.empty_cache()
for epoch in range(epochs):
model.train()
epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, optimizer)
#scheduler.step() #更新学习率（调用官方动态学习率接口时使用）
model.eval()
epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)
#保存最佳模型到best_model
if epoch_test_acc > best_acc:
best_acc = epoch_test_acc
best_model = copy.deepcopy(model)
train_acc.append(epoch_train_acc)
train_loss.append(epoch_train_loss)
test_acc.append(epoch_test_acc)
test_loss.append(epoch_test_loss)
#获取当前的学习率
lr = optimizer.state_dict()['param_groups'][0]['lr']
template = ('Epoch: {:2d}. Train_acc: {:.1f}%, Train_loss: {:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, Lr: {:.2E}')
print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss, lr))
PATH = './J7_best_model.pth'
torch.save(model.state_dict(), PATH)
print('Done')输出结果如下：
2.5 结果可视化
import matplotlib.pyplot as plt
#隐藏警告
import warnings
warnings.filterwarnings("ignore") #忽略警告信息
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
plt.rcParams['figure.dpi'] = 100 #分辨率
epochs_range = range(epochs)
plt.figure(figsize=(12, 3))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, train_acc, label='Training Accuracy')
plt.plot(epochs_range, test_acc, label='Test Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1, 2, 2)
plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()输出结果如下：
3 总结
总体而言，Inception v3主要提出了分解卷积，将大卷积因式分解成小卷积核非对称卷积，体现在数学上就是矩阵的分解，即一个大矩阵可以分解成多个小矩阵相乘。
由于简单的增大Inception网络的规模是不可行的，这样会导致计算效率变低，Inception v3在v2的基础上去除低层辅助分类器，高层辅助分类器加入BN层作为正则化器。将较大的卷积核分解为串联的小卷积核，能够进行维度缩减，同时小卷积核在多次串联后，并不会缩小感受野，进而提取的特征所代表的感受野不受影响。而并联卷积核池化，避免了表征瓶颈。这样同时增加宽度核深度，平衡了网络的宽度和深度，因此提高了网络的质量，优化了网络的特征提取效果。
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